Time Series Methods Based on Growth Curves
Return index and value of maximum
Compute log growth rate of cumulated dataset
FilterResults
Returns forecast of number of periods until peak given KFAS::KFS out...
Returns forecast of number of periods until peak given estimated state...
Plots forecast and realised values of the log cumulative growth rate
Plots the growth rates and slope of the log cumulative growth rate
Plots the growth rates and slope of the log cumulative growth rate
Plots the forecast of new cases (the difference of the cumulated varia...
Plots the forecast of new cases (the difference of the cumulated varia...
Reinitialise a data frame by subtracting the reinit.date row from al...
Base class for estimating time-series growth curve models. Classes `SS...
Class for dynamic Gompertz curve state space model object.
Class for re-initialised dynamic Gompertz curve model
Write a selection of relevant results to disc
The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.